Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations321
Missing cells2026
Missing cells (%)24.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory284.1 KiB
Average record size in memory906.3 B

Variable types

Text2
DateTime1
Categorical16
Numeric7

Alerts

cant_apercibimientos has constant value "1.0" Constant
cant_suspensiones has constant value "1.0" Constant
cluster_k_4 has constant value "3" Constant
Estado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
TipoSocietario is highly overall correlated with provinciaHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 3 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 3 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 6 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with Estado and 9 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with Estado and 11 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 6 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with cant_antecedentes and 2 other fieldsHigh correlation
cant_representante is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_socios is highly overall correlated with cant_MontoLimite and 3 other fieldsHigh correlation
dcant_procesos_adjudicado is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
dmonto_total_adjudicado is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
dtotal_articulos_provee is highly overall correlated with cant_MontoLimite and 3 other fieldsHigh correlation
monto_total_adjudicado is highly overall correlated with cant_Apoderado and 4 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 3 other fieldsHigh correlation
provincia is highly overall correlated with TipoSocietario and 1 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
Estado is highly imbalanced (78.3%) Imbalance
TipoSocietario is highly imbalanced (59.5%) Imbalance
provincia is highly imbalanced (75.9%) Imbalance
cant_Apoderado is highly imbalanced (71.0%) Imbalance
cant_representante is highly imbalanced (62.7%) Imbalance
cant_autenticado is highly imbalanced (58.2%) Imbalance
cant_noAutenticado is highly imbalanced (70.1%) Imbalance
dcant_procesos_adjudicado is highly imbalanced (68.3%) Imbalance
cant_procesos_adjudicado has 44 (13.7%) missing values Missing
monto_total_adjudicado has 44 (13.7%) missing values Missing
cant_socios has 31 (9.7%) missing values Missing
cant_apercibimientos has 319 (99.4%) missing values Missing
cant_suspensiones has 319 (99.4%) missing values Missing
cant_antecedentes has 317 (98.8%) missing values Missing
cant_Apoderado has 129 (40.2%) missing values Missing
cant_representante has 126 (39.3%) missing values Missing
cant_noAutenticado has 289 (90.0%) missing values Missing
cant_MontoLimite has 317 (98.8%) missing values Missing
dmonto_total_adjudicado has 44 (13.7%) missing values Missing
dcant_procesos_adjudicado has 44 (13.7%) missing values Missing
CUIT has unique values Unique
monto_total_adjudicado has 7 (2.2%) zeros Zeros
antiguedad has 71 (22.1%) zeros Zeros

Reproduction

Analysis started2025-06-18 13:06:44.756512
Analysis finished2025-06-18 13:06:50.722654
Duration5.97 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct321
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
2025-06-18T10:06:50.897487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length10.436137
Min length8

Characters and Unicode

Total characters3350
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)100.0%

Sample

1st row880461467
2nd row508600808145
3rd rowJ86153046
4th rowDE815631653
5th row216566270012
ValueCountFrequency (%)
203932784 1
 
0.3%
de209523226 1
 
0.3%
274257204 1
 
0.3%
a28006104 1
 
0.3%
133916524 1
 
0.3%
834491474 1
 
0.3%
3148124273 1
 
0.3%
214349010016 1
 
0.3%
30671381110 1
 
0.3%
30708390808 1
 
0.3%
Other values (311) 311
96.9%
2025-06-18T10:06:51.170862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 493
14.7%
1 421
12.6%
3 361
10.8%
2 292
8.7%
4 281
8.4%
6 270
8.1%
5 270
8.1%
7 260
7.8%
8 245
7.3%
9 229
6.8%
Other values (28) 228
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493
14.7%
1 421
12.6%
3 361
10.8%
2 292
8.7%
4 281
8.4%
6 270
8.1%
5 270
8.1%
7 260
7.8%
8 245
7.3%
9 229
6.8%
Other values (28) 228
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493
14.7%
1 421
12.6%
3 361
10.8%
2 292
8.7%
4 281
8.4%
6 270
8.1%
5 270
8.1%
7 260
7.8%
8 245
7.3%
9 229
6.8%
Other values (28) 228
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493
14.7%
1 421
12.6%
3 361
10.8%
2 292
8.7%
4 281
8.4%
6 270
8.1%
5 270
8.1%
7 260
7.8%
8 245
7.3%
9 229
6.8%
Other values (28) 228
6.8%

Nombre
Text

Distinct311
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2025-06-18T10:06:51.392033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length111
Median length47
Mean length23.267913
Min length3

Characters and Unicode

Total characters7469
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)96.6%

Sample

1st rowMSLI Latam, Inc.
2nd rowWood Mackenzie Limited
3rd rowUNA MÁS UNA S..C
4th rowLighter Than Air GmbH
5th rowLuciano Superville
ValueCountFrequency (%)
inc 61
 
5.4%
de 36
 
3.2%
gmbh 24
 
2.1%
llc 23
 
2.0%
s.a 18
 
1.6%
17
 
1.5%
ltd 15
 
1.3%
y 15
 
1.3%
datos 11
 
1.0%
sin 11
 
1.0%
Other values (680) 899
79.6%
2025-06-18T10:06:51.698827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
809
 
10.8%
A 389
 
5.2%
I 345
 
4.6%
E 306
 
4.1%
S 304
 
4.1%
n 273
 
3.7%
e 270
 
3.6%
a 257
 
3.4%
O 255
 
3.4%
C 255
 
3.4%
Other values (70) 4006
53.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
809
 
10.8%
A 389
 
5.2%
I 345
 
4.6%
E 306
 
4.1%
S 304
 
4.1%
n 273
 
3.7%
e 270
 
3.6%
a 257
 
3.4%
O 255
 
3.4%
C 255
 
3.4%
Other values (70) 4006
53.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
809
 
10.8%
A 389
 
5.2%
I 345
 
4.6%
E 306
 
4.1%
S 304
 
4.1%
n 273
 
3.7%
e 270
 
3.6%
a 257
 
3.4%
O 255
 
3.4%
C 255
 
3.4%
Other values (70) 4006
53.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
809
 
10.8%
A 389
 
5.2%
I 345
 
4.6%
E 306
 
4.1%
S 304
 
4.1%
n 273
 
3.7%
e 270
 
3.6%
a 257
 
3.4%
O 255
 
3.4%
C 255
 
3.4%
Other values (70) 4006
53.6%
Distinct268
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Minimum2016-02-11 00:00:00
Maximum2022-12-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-18T10:06:51.776990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:51.906052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
Pre Inscripto
284 
Desactualizado Por Mantencion Formulario
30 
Con Solicitud De Baja
 
2
Inhabilitado
 
1
Desactualizado Por Documentos Vencidos
 
1
Other values (3)
 
3

Length

Max length40
Median length13
Mean length15.629283
Min length9

Characters and Unicode

Total characters5017
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.6%

Sample

1st rowDesactualizado Por Mantencion Formulario
2nd rowPre Inscripto
3rd rowPre Inscripto
4th rowPre Inscripto
5th rowPre Inscripto

Common Values

ValueCountFrequency (%)
Pre Inscripto 284
88.5%
Desactualizado Por Mantencion Formulario 30
 
9.3%
Con Solicitud De Baja 2
 
0.6%
Inhabilitado 1
 
0.3%
Desactualizado Por Documentos Vencidos 1
 
0.3%
En Evaluacion 1
 
0.3%
Dar De Baja 1
 
0.3%
Inscripto 1
 
0.3%

Length

2025-06-18T10:06:52.016127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:52.094263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 285
40.3%
pre 284
40.2%
desactualizado 31
 
4.4%
por 31
 
4.4%
mantencion 30
 
4.2%
formulario 30
 
4.2%
baja 3
 
0.4%
de 3
 
0.4%
solicitud 2
 
0.3%
con 2
 
0.3%
Other values (6) 6
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r 661
13.2%
o 446
8.9%
386
 
7.7%
i 384
 
7.7%
n 382
 
7.6%
c 351
 
7.0%
e 350
 
7.0%
t 350
 
7.0%
s 318
 
6.3%
P 315
 
6.3%
Other values (20) 1074
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 661
13.2%
o 446
8.9%
386
 
7.7%
i 384
 
7.7%
n 382
 
7.6%
c 351
 
7.0%
e 350
 
7.0%
t 350
 
7.0%
s 318
 
6.3%
P 315
 
6.3%
Other values (20) 1074
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 661
13.2%
o 446
8.9%
386
 
7.7%
i 384
 
7.7%
n 382
 
7.6%
c 351
 
7.0%
e 350
 
7.0%
t 350
 
7.0%
s 318
 
6.3%
P 315
 
6.3%
Other values (20) 1074
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 661
13.2%
o 446
8.9%
386
 
7.7%
i 384
 
7.7%
n 382
 
7.6%
c 351
 
7.0%
e 350
 
7.0%
t 350
 
7.0%
s 318
 
6.3%
P 315
 
6.3%
Other values (20) 1074
21.4%

TipoSocietario
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size50.2 KiB
Persona Jurídica Extranjero Sin Sucursal
256 
Persona Física Extranjero No Residente
 
24
Otras Formas Societarias
 
23
Sociedades De Hecho
 
8
Organismo Publico
 
7
Other values (2)
 
3

Length

Max length40
Median length40
Mean length37.429907
Min length12

Characters and Unicode

Total characters12015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowPersona Jurídica Extranjero Sin Sucursal
2nd rowPersona Jurídica Extranjero Sin Sucursal
3rd rowPersona Jurídica Extranjero Sin Sucursal
4th rowPersona Jurídica Extranjero Sin Sucursal
5th rowPersona Física Extranjero No Residente

Common Values

ValueCountFrequency (%)
Persona Jurídica Extranjero Sin Sucursal 256
79.8%
Persona Física Extranjero No Residente 24
 
7.5%
Otras Formas Societarias 23
 
7.2%
Sociedades De Hecho 8
 
2.5%
Organismo Publico 7
 
2.2%
Cooperativas 2
 
0.6%
Sociedad Anónima 1
 
0.3%

Length

2025-06-18T10:06:52.188747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:52.266850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
persona 280
18.5%
extranjero 280
18.5%
jurídica 256
16.9%
sin 256
16.9%
sucursal 256
16.9%
física 24
 
1.6%
no 24
 
1.6%
residente 24
 
1.6%
otras 23
 
1.5%
formas 23
 
1.5%
Other values (9) 65
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r 1430
11.9%
a 1209
 
10.1%
1190
 
9.9%
n 849
 
7.1%
u 775
 
6.5%
e 690
 
5.7%
s 670
 
5.6%
o 665
 
5.5%
i 632
 
5.3%
c 583
 
4.9%
Other values (25) 3322
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1430
11.9%
a 1209
 
10.1%
1190
 
9.9%
n 849
 
7.1%
u 775
 
6.5%
e 690
 
5.7%
s 670
 
5.6%
o 665
 
5.5%
i 632
 
5.3%
c 583
 
4.9%
Other values (25) 3322
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1430
11.9%
a 1209
 
10.1%
1190
 
9.9%
n 849
 
7.1%
u 775
 
6.5%
e 690
 
5.7%
s 670
 
5.6%
o 665
 
5.5%
i 632
 
5.3%
c 583
 
4.9%
Other values (25) 3322
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1430
11.9%
a 1209
 
10.1%
1190
 
9.9%
n 849
 
7.1%
u 775
 
6.5%
e 690
 
5.7%
s 670
 
5.6%
o 665
 
5.5%
i 632
 
5.3%
c 583
 
4.9%
Other values (25) 3322
27.6%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201913.1
Minimum201608
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:52.360588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201608
5-th percentile201704
Q1201806
median201906
Q3202010
95-th percentile202111
Maximum202211
Range603
Interquartile range (IQR)204

Descriptive statistics

Standard deviation153.86507
Coefficient of variation (CV)0.00076203611
Kurtosis-0.99161213
Mean201913.1
Median Absolute Deviation (MAD)103
Skewness0.036439519
Sum64814106
Variance23674.461
MonotonicityNot monotonic
2025-06-18T10:06:52.470790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202009 18
 
5.6%
202109 14
 
4.4%
201704 13
 
4.0%
201809 13
 
4.0%
201708 11
 
3.4%
201903 11
 
3.4%
201904 10
 
3.1%
201906 9
 
2.8%
201907 9
 
2.8%
202103 8
 
2.5%
Other values (57) 205
63.9%
ValueCountFrequency (%)
201608 1
 
0.3%
201610 1
 
0.3%
201611 3
 
0.9%
201612 2
 
0.6%
201701 1
 
0.3%
201702 3
 
0.9%
201703 3
 
0.9%
201704 13
4.0%
201705 4
 
1.2%
201706 1
 
0.3%
ValueCountFrequency (%)
202211 2
 
0.6%
202209 1
 
0.3%
202208 4
 
1.2%
202204 4
 
1.2%
202202 3
 
0.9%
202112 1
 
0.3%
202111 4
 
1.2%
202110 3
 
0.9%
202109 14
4.4%
202108 2
 
0.6%

anio_preinscripcion
Categorical

High correlation 

Distinct7
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
2019
74 
2017
58 
2021
57 
2020
56 
2018
55 
Other values (2)
21 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1284
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 74
23.1%
2017 58
18.1%
2021 57
17.8%
2020 56
17.4%
2018 55
17.1%
2022 14
 
4.4%
2016 7
 
2.2%

Length

2025-06-18T10:06:52.564525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:52.627018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2019 74
23.1%
2017 58
18.1%
2021 57
17.8%
2020 56
17.4%
2018 55
17.1%
2022 14
 
4.4%
2016 7
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2 462
36.0%
0 377
29.4%
1 251
19.5%
9 74
 
5.8%
7 58
 
4.5%
8 55
 
4.3%
6 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 462
36.0%
0 377
29.4%
1 251
19.5%
9 74
 
5.8%
7 58
 
4.5%
8 55
 
4.3%
6 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 462
36.0%
0 377
29.4%
1 251
19.5%
9 74
 
5.8%
7 58
 
4.5%
8 55
 
4.3%
6 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 462
36.0%
0 377
29.4%
1 251
19.5%
9 74
 
5.8%
7 58
 
4.5%
8 55
 
4.3%
6 7
 
0.5%

cant_procesos_adjudicado
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)4.0%
Missing44
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean2.1046931
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:52.713186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.2
Maximum76
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.9650824
Coefficient of variation (CV)2.3590529
Kurtosis180.36944
Mean2.1046931
Median Absolute Deviation (MAD)0
Skewness12.515762
Sum583
Variance24.652043
MonotonicityNot monotonic
2025-06-18T10:06:52.791233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 179
55.8%
2 52
 
16.2%
3 24
 
7.5%
4 8
 
2.5%
5 5
 
1.6%
6 3
 
0.9%
14 2
 
0.6%
76 1
 
0.3%
9 1
 
0.3%
17 1
 
0.3%
(Missing) 44
 
13.7%
ValueCountFrequency (%)
1 179
55.8%
2 52
 
16.2%
3 24
 
7.5%
4 8
 
2.5%
5 5
 
1.6%
6 3
 
0.9%
9 1
 
0.3%
14 2
 
0.6%
17 1
 
0.3%
23 1
 
0.3%
ValueCountFrequency (%)
76 1
 
0.3%
23 1
 
0.3%
17 1
 
0.3%
14 2
 
0.6%
9 1
 
0.3%
6 3
 
0.9%
5 5
 
1.6%
4 8
 
2.5%
3 24
7.5%
2 52
16.2%

monto_total_adjudicado
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct266
Distinct (%)96.0%
Missing44
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean4047340
Minimum0
Maximum1.8480403 × 108
Zeros7
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:52.881780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1903.5
Q119108.86
median75806.4
Q3493648.4
95-th percentile21172988
Maximum1.8480403 × 108
Range1.8480403 × 108
Interquartile range (IQR)474539.54

Descriptive statistics

Standard deviation17983170
Coefficient of variation (CV)4.4432071
Kurtosis61.395186
Mean4047340
Median Absolute Deviation (MAD)71106.4
Skewness7.3965027
Sum1.1211132 × 109
Variance3.233944 × 1014
MonotonicityNot monotonic
2025-06-18T10:06:52.976550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
2.2%
17500 2
 
0.6%
40000 2
 
0.6%
7000 2
 
0.6%
3500 2
 
0.6%
25000 2
 
0.6%
434829 1
 
0.3%
4951866.857 1
 
0.3%
73645496.62 1
 
0.3%
1105879.228 1
 
0.3%
Other values (256) 256
79.8%
(Missing) 44
 
13.7%
ValueCountFrequency (%)
0 7
2.2%
375.0967742 1
 
0.3%
516.24 1
 
0.3%
680 1
 
0.3%
1300 1
 
0.3%
1497 1
 
0.3%
1550.5 1
 
0.3%
1867.5 1
 
0.3%
1912.5 1
 
0.3%
2450 1
 
0.3%
ValueCountFrequency (%)
184804026.6 1
0.3%
143640949.5 1
0.3%
139233387.8 1
0.3%
73645496.62 1
0.3%
56668408.84 1
0.3%
49302094.77 1
0.3%
33660000 1
0.3%
32156000.61 1
0.3%
31678000 1
0.3%
27417600 1
0.3%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9813084
Minimum0
Maximum5
Zeros71
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:53.070287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4682982
Coefficient of variation (CV)0.74107502
Kurtosis-1.1445713
Mean1.9813084
Median Absolute Deviation (MAD)1
Skewness0.12187694
Sum636
Variance2.1558995
MonotonicityNot monotonic
2025-06-18T10:06:53.135663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 74
23.1%
0 71
22.1%
4 58
18.1%
1 56
17.4%
3 55
17.1%
5 7
 
2.2%
ValueCountFrequency (%)
0 71
22.1%
1 56
17.4%
2 74
23.1%
3 55
17.1%
4 58
18.1%
5 7
 
2.2%
ValueCountFrequency (%)
5 7
 
2.2%
4 58
18.1%
3 55
17.1%
2 74
23.1%
1 56
17.4%
0 71
22.1%

provincia
Categorical

High correlation  Imbalance 

Distinct19
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size23.7 KiB
Extranjera
280 
Buenos Aires
 
11
Santa Fe
 
4
Córdoba
 
3
Chubut
 
3
Other values (14)
 
20

Length

Max length16
Median length10
Mean length9.8722741
Min length5

Characters and Unicode

Total characters3169
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.5%

Sample

1st rowExtranjera
2nd rowExtranjera
3rd rowExtranjera
4th rowExtranjera
5th rowExtranjera

Common Values

ValueCountFrequency (%)
Extranjera 280
87.2%
Buenos Aires 11
 
3.4%
Santa Fe 4
 
1.2%
Córdoba 3
 
0.9%
Chubut 3
 
0.9%
San Juan 2
 
0.6%
Mendoza 2
 
0.6%
La Pampa 2
 
0.6%
Santa Cruz 2
 
0.6%
Neuquén 2
 
0.6%
Other values (9) 10
 
3.1%

Length

2025-06-18T10:06:53.213784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
extranjera 280
80.5%
buenos 11
 
3.2%
aires 11
 
3.2%
santa 6
 
1.7%
fe 4
 
1.1%
córdoba 3
 
0.9%
chubut 3
 
0.9%
san 3
 
0.9%
la 3
 
0.9%
juan 2
 
0.6%
Other values (17) 22
 
6.3%

Most occurring characters

ValueCountFrequency (%)
a 593
18.7%
r 581
18.3%
e 317
10.0%
n 310
9.8%
t 290
9.2%
E 281
8.9%
j 281
8.9%
x 280
8.8%
u 29
 
0.9%
s 29
 
0.9%
Other values (28) 178
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 593
18.7%
r 581
18.3%
e 317
10.0%
n 310
9.8%
t 290
9.2%
E 281
8.9%
j 281
8.9%
x 280
8.8%
u 29
 
0.9%
s 29
 
0.9%
Other values (28) 178
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 593
18.7%
r 581
18.3%
e 317
10.0%
n 310
9.8%
t 290
9.2%
E 281
8.9%
j 281
8.9%
x 280
8.8%
u 29
 
0.9%
s 29
 
0.9%
Other values (28) 178
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 593
18.7%
r 581
18.3%
e 317
10.0%
n 310
9.8%
t 290
9.2%
E 281
8.9%
j 281
8.9%
x 280
8.8%
u 29
 
0.9%
s 29
 
0.9%
Other values (28) 178
 
5.6%

cant_socios
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)3.4%
Missing31
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1.2275862
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:53.276277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1451406
Coefficient of variation (CV)0.93283929
Kurtosis53.303649
Mean1.2275862
Median Absolute Deviation (MAD)0
Skewness6.7839605
Sum356
Variance1.3113471
MonotonicityNot monotonic
2025-06-18T10:06:53.354393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 271
84.4%
2 6
 
1.9%
3 5
 
1.6%
6 2
 
0.6%
5 1
 
0.3%
13 1
 
0.3%
4 1
 
0.3%
9 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
(Missing) 31
 
9.7%
ValueCountFrequency (%)
1 271
84.4%
2 6
 
1.9%
3 5
 
1.6%
4 1
 
0.3%
5 1
 
0.3%
6 2
 
0.6%
7 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
13 1
 
0.3%
ValueCountFrequency (%)
13 1
 
0.3%
9 1
 
0.3%
8 1
 
0.3%
7 1
 
0.3%
6 2
 
0.6%
5 1
 
0.3%
4 1
 
0.3%
3 5
 
1.6%
2 6
 
1.9%
1 271
84.4%

cant_apercibimientos
Categorical

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing319
Missing (%)99.4%
Memory size20.1 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
0.6%
(Missing) 319
99.4%

Length

2025-06-18T10:06:53.431590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:53.480379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

cant_suspensiones
Categorical

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing319
Missing (%)99.4%
Memory size20.1 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
0.6%
(Missing) 319
99.4%

Length

2025-06-18T10:06:53.527254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:53.574058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

cant_antecedentes
Categorical

High correlation  Missing 

Distinct2
Distinct (%)50.0%
Missing317
Missing (%)98.8%
Memory size20.1 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
0.9%
2.0 1
 
0.3%
(Missing) 317
98.8%

Length

2025-06-18T10:06:53.829474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:53.885005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
75.0%
2.0 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

cant_Apoderado
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)2.6%
Missing129
Missing (%)40.2%
Memory size20.8 KiB
1.0
169 
2.0
17 
3.0
 
3
6.0
 
2
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row6.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 169
52.6%
2.0 17
 
5.3%
3.0 3
 
0.9%
6.0 2
 
0.6%
5.0 1
 
0.3%
(Missing) 129
40.2%

Length

2025-06-18T10:06:53.957126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:54.032320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 169
88.0%
2.0 17
 
8.9%
3.0 3
 
1.6%
6.0 2
 
1.0%
5.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
1 169
29.3%
2 17
 
3.0%
3 3
 
0.5%
6 2
 
0.3%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
1 169
29.3%
2 17
 
3.0%
3 3
 
0.5%
6 2
 
0.3%
5 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
1 169
29.3%
2 17
 
3.0%
3 3
 
0.5%
6 2
 
0.3%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
1 169
29.3%
2 17
 
3.0%
3 3
 
0.5%
6 2
 
0.3%
5 1
 
0.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)1.0%
Missing126
Missing (%)39.3%
Memory size20.8 KiB
1.0
181 
2.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters585
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 181
56.4%
2.0 14
 
4.4%
(Missing) 126
39.3%

Length

2025-06-18T10:06:54.107399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:54.165300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 181
92.8%
2.0 14
 
7.2%

Most occurring characters

ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 181
30.9%
2 14
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 181
30.9%
2 14
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 181
30.9%
2 14
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 195
33.3%
0 195
33.3%
1 181
30.9%
2 14
 
2.4%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
1.0
259 
2.0
54 
3.0
 
6
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters963
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 259
80.7%
2.0 54
 
16.8%
3.0 6
 
1.9%
5.0 2
 
0.6%

Length

2025-06-18T10:06:54.232341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:54.292921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 259
80.7%
2.0 54
 
16.8%
3.0 6
 
1.9%
5.0 2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 321
33.3%
0 321
33.3%
1 259
26.9%
2 54
 
5.6%
3 6
 
0.6%
5 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 321
33.3%
0 321
33.3%
1 259
26.9%
2 54
 
5.6%
3 6
 
0.6%
5 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 321
33.3%
0 321
33.3%
1 259
26.9%
2 54
 
5.6%
3 6
 
0.6%
5 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 321
33.3%
0 321
33.3%
1 259
26.9%
2 54
 
5.6%
3 6
 
0.6%
5 2
 
0.2%

cant_noAutenticado
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)12.5%
Missing289
Missing (%)90.0%
Memory size20.2 KiB
1.0
29 
5.0
 
1
6.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)9.4%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 29
 
9.0%
5.0 1
 
0.3%
6.0 1
 
0.3%
3.0 1
 
0.3%
(Missing) 289
90.0%

Length

2025-06-18T10:06:54.375140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:54.439508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 29
90.6%
5.0 1
 
3.1%
6.0 1
 
3.1%
3.0 1
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 32
33.3%
0 32
33.3%
1 29
30.2%
5 1
 
1.0%
6 1
 
1.0%
3 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 32
33.3%
0 32
33.3%
1 29
30.2%
5 1
 
1.0%
6 1
 
1.0%
3 1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 32
33.3%
0 32
33.3%
1 29
30.2%
5 1
 
1.0%
6 1
 
1.0%
3 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 32
33.3%
0 32
33.3%
1 29
30.2%
5 1
 
1.0%
6 1
 
1.0%
3 1
 
1.0%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.9%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1.3616352
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:54.490092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7310421
Coefficient of variation (CV)0.53688542
Kurtosis20.077726
Mean1.3616352
Median Absolute Deviation (MAD)0
Skewness3.7065931
Sum433
Variance0.53442255
MonotonicityNot monotonic
2025-06-18T10:06:54.566138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 226
70.4%
2 82
 
25.5%
3 5
 
1.6%
5 3
 
0.9%
6 1
 
0.3%
7 1
 
0.3%
(Missing) 3
 
0.9%
ValueCountFrequency (%)
1 226
70.4%
2 82
 
25.5%
3 5
 
1.6%
5 3
 
0.9%
6 1
 
0.3%
7 1
 
0.3%
ValueCountFrequency (%)
7 1
 
0.3%
6 1
 
0.3%
5 3
 
0.9%
3 5
 
1.6%
2 82
 
25.5%
1 226
70.4%

cant_MontoLimite
Categorical

High correlation  Missing 

Distinct2
Distinct (%)50.0%
Missing317
Missing (%)98.8%
Memory size20.1 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0

Common Values

ValueCountFrequency (%)
1.0 3
 
0.9%
2.0 1
 
0.3%
(Missing) 317
98.8%

Length

2025-06-18T10:06:54.698460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:54.748140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
75.0%
2.0 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 3
25.0%
2 1
 
8.3%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6853583
Minimum1
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2025-06-18T10:06:54.823536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile28
Maximum515
Range514
Interquartile range (IQR)5

Descriptive statistics

Standard deviation34.0647
Coefficient of variation (CV)3.9220835
Kurtosis160.22169
Mean8.6853583
Median Absolute Deviation (MAD)1
Skewness11.658843
Sum2788
Variance1160.4038
MonotonicityNot monotonic
2025-06-18T10:06:54.940951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 149
46.4%
2 33
 
10.3%
3 27
 
8.4%
6 15
 
4.7%
4 14
 
4.4%
7 11
 
3.4%
5 9
 
2.8%
8 6
 
1.9%
10 5
 
1.6%
11 4
 
1.2%
Other values (31) 48
 
15.0%
ValueCountFrequency (%)
1 149
46.4%
2 33
 
10.3%
3 27
 
8.4%
4 14
 
4.4%
5 9
 
2.8%
6 15
 
4.7%
7 11
 
3.4%
8 6
 
1.9%
9 4
 
1.2%
10 5
 
1.6%
ValueCountFrequency (%)
515 1
0.3%
237 1
0.3%
153 1
0.3%
118 1
0.3%
58 1
0.3%
53 1
0.3%
50 2
0.6%
49 1
0.3%
45 1
0.3%
43 1
0.3%

dmonto_total_adjudicado
Categorical

High correlation  Missing 

Distinct19
Distinct (%)6.9%
Missing44
Missing (%)13.7%
Memory size26.4 KiB
(-0.001, 33011.111]
98 
(33011.111, 104767.373]
48 
(104767.373, 224078.198]
33 
(224078.198, 377939.298]
25 
(1793326.755, 2483085.385]
 
9
Other values (14)
64 

Length

Max length28
Median length27
Mean length22.267148
Min length19

Characters and Unicode

Total characters6168
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(89439449.702, 222964579.98]
2nd row(3396600.0, 4727330.113]
3rd row(104767.373, 224078.198]
4th row(-0.001, 33011.111]
5th row(377939.298, 599760.0]

Common Values

ValueCountFrequency (%)
(-0.001, 33011.111] 98
30.5%
(33011.111, 104767.373] 48
15.0%
(104767.373, 224078.198] 33
 
10.3%
(224078.198, 377939.298] 25
 
7.8%
(1793326.755, 2483085.385] 9
 
2.8%
(890758.9, 1302657.558] 9
 
2.8%
(377939.298, 599760.0] 9
 
2.8%
(6702697.888, 9424898.401] 7
 
2.2%
(19975532.58, 30451916.51] 6
 
1.9%
(599760.0, 890758.9] 5
 
1.6%
Other values (9) 28
 
8.7%
(Missing) 44
13.7%

Length

2025-06-18T10:06:55.040257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
33011.111 146
26.4%
0.001 98
17.7%
104767.373 81
14.6%
224078.198 58
 
10.5%
377939.298 34
 
6.1%
1793326.755 14
 
2.5%
890758.9 14
 
2.5%
1302657.558 14
 
2.5%
599760.0 14
 
2.5%
2483085.385 12
 
2.2%
Other values (10) 69
12.5%

Most occurring characters

ValueCountFrequency (%)
1 1079
17.5%
0 703
11.4%
3 645
10.5%
. 554
9.0%
7 519
8.4%
9 318
 
5.2%
8 308
 
5.0%
, 277
 
4.5%
( 277
 
4.5%
277
 
4.5%
Other values (6) 1211
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1079
17.5%
0 703
11.4%
3 645
10.5%
. 554
9.0%
7 519
8.4%
9 318
 
5.2%
8 308
 
5.0%
, 277
 
4.5%
( 277
 
4.5%
277
 
4.5%
Other values (6) 1211
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1079
17.5%
0 703
11.4%
3 645
10.5%
. 554
9.0%
7 519
8.4%
9 318
 
5.2%
8 308
 
5.0%
, 277
 
4.5%
( 277
 
4.5%
277
 
4.5%
Other values (6) 1211
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1079
17.5%
0 703
11.4%
3 645
10.5%
. 554
9.0%
7 519
8.4%
9 318
 
5.2%
8 308
 
5.0%
, 277
 
4.5%
( 277
 
4.5%
277
 
4.5%
Other values (6) 1211
19.6%

dcant_procesos_adjudicado
Categorical

High correlation  Imbalance  Missing 

Distinct9
Distinct (%)3.2%
Missing44
Missing (%)13.7%
Memory size23.5 KiB
(0.999, 2.0]
231 
(2.0, 3.0]
24 
(3.0, 4.0]
 
8
(4.0, 5.0]
 
5
(5.0, 6.0]
 
3
Other values (4)
 
6

Length

Max length14
Median length12
Mean length11.714801
Min length10

Characters and Unicode

Total characters3245
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.1%

Sample

1st row(39.0, 1214.0]
2nd row(0.999, 2.0]
3rd row(0.999, 2.0]
4th row(0.999, 2.0]
5th row(4.0, 5.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 231
72.0%
(2.0, 3.0] 24
 
7.5%
(3.0, 4.0] 8
 
2.5%
(4.0, 5.0] 5
 
1.6%
(5.0, 6.0] 3
 
0.9%
(12.0, 19.0] 3
 
0.9%
(39.0, 1214.0] 1
 
0.3%
(8.0, 12.0] 1
 
0.3%
(19.0, 39.0] 1
 
0.3%
(Missing) 44
 
13.7%

Length

2025-06-18T10:06:55.119205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:55.197993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 255
46.0%
0.999 231
41.7%
3.0 32
 
5.8%
4.0 13
 
2.3%
5.0 8
 
1.4%
12.0 4
 
0.7%
19.0 4
 
0.7%
6.0 3
 
0.5%
39.0 2
 
0.4%
1214.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
9 699
21.5%
0 554
17.1%
. 554
17.1%
( 277
 
8.5%
, 277
 
8.5%
277
 
8.5%
] 277
 
8.5%
2 260
 
8.0%
3 34
 
1.0%
4 14
 
0.4%
Other values (4) 22
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 699
21.5%
0 554
17.1%
. 554
17.1%
( 277
 
8.5%
, 277
 
8.5%
277
 
8.5%
] 277
 
8.5%
2 260
 
8.0%
3 34
 
1.0%
4 14
 
0.4%
Other values (4) 22
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 699
21.5%
0 554
17.1%
. 554
17.1%
( 277
 
8.5%
, 277
 
8.5%
277
 
8.5%
] 277
 
8.5%
2 260
 
8.0%
3 34
 
1.0%
4 14
 
0.4%
Other values (4) 22
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 699
21.5%
0 554
17.1%
. 554
17.1%
( 277
 
8.5%
, 277
 
8.5%
277
 
8.5%
] 277
 
8.5%
2 260
 
8.0%
3 34
 
1.0%
4 14
 
0.4%
Other values (4) 22
 
0.7%

dtotal_articulos_provee
Categorical

High correlation 

Distinct14
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
(0.999, 2.0]
182 
(2.0, 3.0]
27 
(4.0, 6.0]
24 
(6.0, 8.0]
 
17
(3.0, 4.0]
 
14
Other values (9)
57 

Length

Max length15
Median length12
Mean length11.470405
Min length10

Characters and Unicode

Total characters3682
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row(15.0, 21.0]
2nd row(4.0, 6.0]
3rd row(15.0, 21.0]
4th row(0.999, 2.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 182
56.7%
(2.0, 3.0] 27
 
8.4%
(4.0, 6.0] 24
 
7.5%
(6.0, 8.0] 17
 
5.3%
(3.0, 4.0] 14
 
4.4%
(8.0, 11.0] 13
 
4.0%
(11.0, 15.0] 11
 
3.4%
(15.0, 21.0] 10
 
3.1%
(40.0, 58.0] 7
 
2.2%
(21.0, 29.0] 7
 
2.2%
Other values (4) 9
 
2.8%

Length

2025-06-18T10:06:55.291734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 209
32.6%
0.999 182
28.3%
3.0 41
 
6.4%
6.0 41
 
6.4%
4.0 38
 
5.9%
8.0 30
 
4.7%
11.0 24
 
3.7%
15.0 21
 
3.3%
21.0 17
 
2.6%
40.0 12
 
1.9%
Other values (6) 27
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 652
17.7%
. 642
17.4%
9 562
15.3%
( 321
8.7%
, 321
8.7%
321
8.7%
] 321
8.7%
2 238
 
6.5%
1 92
 
2.5%
4 52
 
1.4%
Other values (5) 160
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 652
17.7%
. 642
17.4%
9 562
15.3%
( 321
8.7%
, 321
8.7%
321
8.7%
] 321
8.7%
2 238
 
6.5%
1 92
 
2.5%
4 52
 
1.4%
Other values (5) 160
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 652
17.7%
. 642
17.4%
9 562
15.3%
( 321
8.7%
, 321
8.7%
321
8.7%
] 321
8.7%
2 238
 
6.5%
1 92
 
2.5%
4 52
 
1.4%
Other values (5) 160
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 652
17.7%
. 642
17.4%
9 562
15.3%
( 321
8.7%
, 321
8.7%
321
8.7%
] 321
8.7%
2 238
 
6.5%
1 92
 
2.5%
4 52
 
1.4%
Other values (5) 160
 
4.3%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
3
321 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters321
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 321
100.0%

Length

2025-06-18T10:06:55.373571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:06:55.412627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 321
100.0%

Most occurring characters

ValueCountFrequency (%)
3 321
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 321
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 321
100.0%

Interactions

2025-06-18T10:06:49.423412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:45.841894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.414689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.997035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.602136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.151768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.856914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.497975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:45.931444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.506008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.080564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.690132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.399029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.931093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.573430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.014709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.581050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.163312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.773232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.477458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.014179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.667794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.097769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.684525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.251475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.852504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.547581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.106458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.731192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.172239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.748097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.347599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.928434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.635062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.177293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.814731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.247880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.839849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.431036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.996137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.712587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.247776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.893996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.331281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:46.914508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:47.513331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.072116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:48.786401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:06:49.334882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-18T10:06:55.489913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_sociosdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.1140.0000.0000.1790.7071.0000.1690.3020.0000.2450.1230.0000.0000.0000.2810.0000.0000.3000.483
TipoSocietario0.1141.0000.1420.1160.0000.0000.0000.2190.0000.0000.2230.1790.4430.0000.2420.0000.0860.1390.6560.146
anio_preinscripcion0.0000.1421.0000.9980.2190.7070.7070.0000.2550.2000.1810.1650.1100.1510.1530.1200.1800.9980.1370.102
antiguedad0.0000.1160.9981.0000.2080.7070.7070.0000.3170.1320.1900.084-0.0980.1810.1600.1360.051-0.9810.000-0.008
cant_Apoderado0.1790.0000.2190.2081.0001.0001.0000.5430.7930.4830.6680.6900.0000.3850.2970.3110.5020.2070.0000.142
cant_MontoLimite0.7070.0000.7070.7071.0001.0000.0000.0000.0000.0001.000NaN1.0000.0000.0001.0001.0000.7070.0001.000
cant_antecedentes1.0000.0000.7070.7071.0000.0001.0000.000NaN1.0001.0000.0001.0001.0001.0001.0001.0000.7070.0000.000
cant_autenticado0.1690.2190.0000.0000.5430.0000.0001.0000.9660.0000.4440.8240.2340.0000.3430.1250.5690.0000.0840.000
cant_noAutenticado0.3020.0000.2550.3170.7930.000NaN0.9661.0000.9560.4991.0001.0000.4440.3910.5700.4910.1710.0000.966
cant_procesos_adjudicado0.0000.0000.2000.1320.4830.0001.0000.0000.9561.0000.0000.009-0.0220.8910.3510.3910.280-0.1740.0000.159
cant_representante0.2450.2230.1810.1900.6681.0001.0000.4440.4990.0001.0000.4970.2060.0000.0000.3660.2560.1670.0000.239
cant_sinMontoLimite0.1230.1790.1650.0840.690NaN0.0000.8241.0000.0090.4971.000-0.0080.4300.3000.2230.092-0.0930.0000.138
cant_socios0.0000.4430.110-0.0980.0001.0001.0000.2341.000-0.0220.206-0.0081.0000.0000.3050.0000.2010.0940.586-0.066
dcant_procesos_adjudicado0.0000.0000.1510.1810.3850.0001.0000.0000.4440.8910.0000.4300.0001.0000.3060.2180.4780.1320.0000.000
dmonto_total_adjudicado0.0000.2420.1530.1600.2970.0001.0000.3430.3910.3510.0000.3000.3050.3061.0000.0000.7290.1240.1530.000
dtotal_articulos_provee0.2810.0000.1200.1360.3111.0001.0000.1250.5700.3910.3660.2230.0000.2180.0001.0000.0000.1280.1260.889
monto_total_adjudicado0.0000.0860.1800.0510.5021.0001.0000.5690.4910.2800.2560.0920.2010.4780.7290.0001.000-0.0610.000-0.009
periodo_preinscripcion0.0000.1390.998-0.9810.2070.7070.7070.0000.171-0.1740.167-0.0930.0940.1320.1240.128-0.0611.0000.153-0.021
provincia0.3000.6560.1370.0000.0000.0000.0000.0840.0000.0000.0000.0000.5860.0000.1530.1260.0000.1531.0000.379
total_articulos_provee0.4830.1460.102-0.0080.1421.0000.0000.0000.9660.1590.2390.138-0.0660.0000.0000.889-0.009-0.0210.3791.000

Missing values

2025-06-18T10:06:50.077518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-18T10:06:50.298427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-18T10:06:50.521815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
62880461467MSLI Latam, Inc.26/10/2016Desactualizado Por Mantencion FormularioPersona Jurídica Extranjero Sin Sucursal201610201676.01.848040e+085.0Extranjera1.0NaNNaNNaN6.0NaN1.05.06.0NaN20.0(89439449.702, 222964579.98](39.0, 1214.0](15.0, 21.0]3
119508600808145Wood Mackenzie Limited02/11/2016Pre InscriptoPersona Jurídica Extranjero Sin Sucursal2016112016NaNNaN5.0Extranjera1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN5.0NaNNaN(4.0, 6.0]3
191J86153046UNA MÁS UNA S..C28/11/2016Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20161120161.04.037500e+065.0Extranjera1.0NaNNaNNaN1.01.01.01.02.0NaN16.0(3396600.0, 4727330.113](0.999, 2.0](15.0, 21.0]3
485DE815631653Lighter Than Air GmbH06/04/2017Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20170420171.01.139601e+054.0Extranjera1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(104767.373, 224078.198](0.999, 2.0](0.999, 2.0]3
487216566270012Luciano Superville14/08/2017Pre InscriptoPersona Física Extranjero No Residente2017082017NaNNaN4.0ExtranjeraNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0NaNNaN(0.999, 2.0]3
50530605416280ALBERTO J MACUA S.A.18/04/2017Pre InscriptoSociedad Anónima20170420171.01.083750e+044.0Santa Fe6.0NaNNaNNaN1.02.03.0NaN3.0NaN2.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]3
521361258310Instron, a division of Illinois Tool31/08/2017Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20170820175.04.348290e+054.0Extranjera1.0NaNNaNNaN2.0NaN2.0NaN2.0NaN8.0(377939.298, 599760.0](4.0, 5.0](6.0, 8.0]3
584B98323850Ingeniería de Presas SL13/09/2017Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20170920171.04.951867e+064.0Extranjera1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(4727330.113, 6702697.888](0.999, 2.0](0.999, 2.0]3
643203932784DOT LIB INFORMATION LLC20/04/2017Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20170420173.07.364550e+074.0Extranjera1.0NaNNaNNaN1.01.01.01.02.0NaN1.0(46718747.516, 89439449.702](2.0, 3.0](0.999, 2.0]3
68530710874081Dar Arte por el Desarrollo Cultural29/11/2016Pre InscriptoOtras Formas Societarias20161120163.01.105879e+065.0Buenos Aires1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(890758.9, 1302657.558](2.0, 3.0](0.999, 2.0]3
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
9722931234758Stevens Water Monitoring Systems, Inc.26/04/2022Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20220420221.026900.000.0Extranjera1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]3
9769046058420JEOL USA, INC20/04/2022Desactualizado Por Mantencion FormularioPersona Jurídica Extranjero Sin Sucursal2022042022NaNNaN0.0Extranjera1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN3.0NaNNaN(2.0, 3.0]3
9815941630162BERKELEY NUCLEONICS CORP01/11/2019Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20191120191.018760.002.0Extranjera1.0NaNNaNNaNNaN1.01.0NaN1.0NaN4.0(-0.001, 33011.111](0.999, 2.0](3.0, 4.0]3
9896205504368Adani Systems, Inc.26/11/2021Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20211120211.063850.000.0Extranjera1.0NaNNaNNaN1.01.02.0NaN2.0NaN2.0(33011.111, 104767.373](0.999, 2.0](0.999, 2.0]3
98999149433147Pinkmatter (Pty) Ltd12/08/2022Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20220820221.0175000.000.0Extranjera1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(104767.373, 224078.198](0.999, 2.0](0.999, 2.0]3
9903STR1708043J2SINECTECH TRAINING SAPI DE CV28/04/2022Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20220420221.03950.000.0Extranjera1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]3
9941580909626Micromeritics Instrument Corporation30/11/2021Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20211120211.025713.000.0Extranjera1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]3
997720061791741098ANALYTICAL TECHNOLOGIES S.A.02/08/2022Desactualizado Por Mantencion FormularioPersona Jurídica Extranjero Sin Sucursal2022082022NaNNaN0.0Extranjera1.0NaNNaNNaN1.01.02.0NaN2.0NaN4.0NaNNaN(3.0, 4.0]3
1000930708683325Condominio Pje. Storni 74519/04/2022Pre InscriptoOtras Formas Societarias20220420221.023480816.330.0Santa Fe8.0NaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(19975532.58, 30451916.51](0.999, 2.0](0.999, 2.0]3
1005530711731721MINISTERIO DE GOBIERNO30/08/2022Pre InscriptoOrganismo Publico20220820221.00.000.0Rio NegroNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN3.0(-0.001, 33011.111](0.999, 2.0](2.0, 3.0]3